Community preventive behaviour and perception on the severity of COVID-19 disease in Indonesia, 2021-2022: Structural equation modelling

Background This study investigated the determinants of community preventive behavior in complying with the Indonesian regulations to prevent COVID-19 local transmission. Methods A cross-sectional study used to collect the data via an online cross using a form created from a google questionnaire forms. A total of 1,802 respondents were gathered at a single point in time. The authors used the Health Belief Model (HBM) approach to measure and create a model for the prevention of local transmission of COVID-19. Results This study found that more than half of the respondents still had low perceived susceptibility (16%) and severity (43%). There were only 3% respondents with perceived barriers and 19% with strong self-efficacy. The findings showed that self-efficacy and perceived barriers had statistically significant relationships with preventive behavior (p-value <0.05). The goodness of fit index showed that the proposed model was not fit for the data (RMSE<0.080, GFI>0.950, AGFI>0.950, SRMR<0.100), which means that it was not fit to describe the empirical phenomenon under study. Conclusions This study found that more than half of the respondents still had low perceived susceptibility (84%) and severity (67%), but more than half had high perceived benefits (54%). Only a few respondents had significant barriers to implementing COVID-19 transmission prevention behaviours (3%). Still, most respondents had low perceived self-efficacy (81%), and only 60% had good behaviours related to COVID-19 prevention. In the context of COVID-19 preventive behaviour, we recommended to improve perceived susceptibility and severity by providing the correct information (which contain information about how people susceptible to the virus and the impact of infected by the virus) with the local cultural context.


Introduction
The world is currently besieged by the COVID-19 pandemic. 1As of February 7, 2022, there were 394,381,395 confirmed cases of COVID-19, with the World Health Organization (WHO) reporting 5,735,179 deaths. 2 The COVID-19 pandemic in Indonesia from mid-2021 until the end of 2021 reached its peak (second wave) when it was dominated by the Delta variant with the total confirmed cases was 4.254.443and 143.766 death (CFR: 3.4). 3VID-19 prevention regulations largely depend on community compliance and behavior.While changing behavior is a significant challenge in health interventions.An AC Nielsen survey (2020) in six major cities with a total of 2,000 respondents, in collaboration with the United Nations International Children's Emergency Fund (UNICEF), found that less than one-third (31.5%) of all respondents practiced all three-preventive activities (wearing masks, washing hands, and social distancing).5][6][7][8][9][10] Based on the official government report (see https://covid19.go.id/monitoring-kepatuhan-protokol-kesehatan), some areas of Indonesia showed a compliance rate of only 60% till I January 2022. 11The aim of this study was to examine

REVISED Amendments from Version 2
There are no major difference between the previous version with the latest.We added a statement in part of HBM conceptual, we added one reference in discussion as suggested, and we added statement on data collection limitation.As additional, as we reviewed the references, we also added several incomplete references (which was marked with blue text before).
changes in community behaviour to prevent local COVID-19 transmissions and changes in community perceptions about the severity level of COVID-19.The following research questions were formulated: 1) What constitutes as the community perceptions of COVID-19?2) Is there an association between community perception and community preventive behaviour using the Health Belief Model (HBM) approach?And 3) what kind of model of preventive behaviour on COVID-19 can be predicted using the structural equation model (SEM)?

Study design and samples
This was a cross-sectional study using Google forms with structured survey questionnaires.The questionnaires have been tested with 30 respondents before the survey was conducted.The proportion of a large population was used to figure out the sample size for a variable, which was used to figure out the minimum sample size. 12The margin error=5%, p=50%, and Zα=95%.The minimum sample size for this study was 385 respondents.The data collection was conducted over two weeks from July 14-26, 2021, using WhatsApp, Line, and Telegram.To expand the coverage throughout Indonesia, social media influencers were asked to distribute the survey through Twitter and Instagram.The respondents who participated in this study were aged 15-62 and Indonesian citizens.On average, it took 20-25 minutes to complete the form.Respondents signed informed consent forms before completing the survey.The total final number of respondents was 1,802.

Conceptual model
We proposed a comprehensive approach to understanding behaviour change using the Health Belief Model rather than study it partially.The HBM offers a holistic perspective on behaviour change processes.However, when employing HBM as a theoretical framework, there were latent variables that did not directly observed.These latent variables are represented by various indicators/observe variable.Consequently, Structural Equation Modelling (SEM) is the most suitable analytical method for examining relationships between variables (latent variable and measurement/indicator) within the HBM framework, as it allows for the analysis of both latent variables and the overall model as a single, cohesive unit.
The HBM considers several main concepts/theories that predict why people will take action to prevent, including individual characteristics, perceived susceptibility, severity, benefit, and self-efficacy. 13Since this study aims to observe the behavior change especially preventive health behavior on COVID-19, the HBM conceptual is one of the complete and holistic frameworks that can be used to figure out how a behavior occurs and change.Theories and models serve the purpose of explaining behavior and offering strategies for promoting behavioral changes.An explanatory theory, serves to elucidate and understand the root causes of a problem.These theories also forecast behaviors in specific circumstances and assist in identifying factors that can be altered, such as knowledge, attitudes, self-efficacy, social support, and resource availability.Theories and models can be applied to investigate the reasons behind people not adhering to public health or medical advice and neglecting their health.They can aid in pinpointing the essential information required before developing and structuring an intervention program. 14

Community preventive behavior against COVID-19 transmission
Community preventive behavior was represented by six indicators in Figure 1 which include frequency of hosting (BHV1), frequency of visiting others (BHV2), frequency of work/study from office/school (BHV3), frequency of handshaking (BHV4), frequency travelling to a red zone (BHV5), and frequency of leaving the house when you are not feeling well (BHV6).

Data analysis
This study is a kind of behavioral sciences that intended to study theoretical construct which is cannot be observed directly-called as latent variables.Thus, we operationally define the latent variable of interest in terms of behavior believed to represent it.In this study, the latent variable includes perceived susceptibility, perceived severity, perceived benefit, perceived barriers, and perceived self-efficacy and for each latent variables, there are observed variables which is based on theory are representing each of latent variables.SEM analysis allowed us to not only analyze observed measurement variable, but it can incorporate both unobserved (latent) and observed variables.One of the specialties of SEM is the hypothesized model can then be tested statistically in a simultaneous analysis of the entire system of variables to determine the extent to which it is consistent with the data.If goodness-of-fit is adequate, the model argues for the plausibility of postulated relations among variables; if it is inadequate, the tenability of such relations is rejected. 16The sample size required using SEM analysis in this study is sufficient (n=1802).The minimum sample size for SEM analysis with seven or less constructs and no under identified constructs is 150. 17e authors used Lisrel version 8.8 software to construct the covariance-based SEM.SEM analysis can also be done using R. Steps of doing SEM analysis using R as follows 18    The six latent variables were perceived susceptibility, severity, benefit, barriers, self-efficacy, and preventive behavior.The 45 observed variables were presented in Table 1 include construct that build by all the observed variables.
Descriptive statistics were presented as numbers and percentages for individual characteristics, and bar charts for perceptions and behavior.Descriptive statistical analysis was performed using IBM Statistical Package for the Social Sciences (SPSS) version 27 and presented using Microsoft Excel.The descriptive analysis simply can also be completed using Microsoft Excel.The SEM analysis was carried out using the following steps: 1) Identifying the degree of freedom of the structural model.

BHV5
Frequency travelling to a red zone.

BHV6
Frequency of leaving the house when you are not feeling well.
4) Assessing structural model validity. 14,19rceived was categorized based on the total score.Good/high perceived were those who chose to "strongly agree" on every question.Regarding behavior, good behavior occurs if the respondent states never or rarely.Rarely was included in the category based on the assumption that people might be challenged to practice preventive behavior related to other factors that require them to leave, such as the environment or critical social activities that cannot be abandoned.

Results
The study results are presented in several parts sequentially, starting with the respondent's characteristics, followed by the descriptive statistics of the independent variable (community perception of the level of severity of COVID-19 disease), the descriptive statistics of the dependent variable (composite variable of community behavior), and the SEM analysis results.
Most of the respondents lived in the city (46.1%) and a housing area (36.1%), and only a few lived-in villages (16.6%).A large majority of the respondents were women (74.5%), and approximately 80.0% were students and workers (Table 2).
In terms of perceived susceptibility (Table 3), more than half of respondents (>50%) chose to strongly agree to practice recommended behaviors such as social distancing at least 1-2 meters in public areas; almost half of them strongly agree that they had a low risk of getting COVID-19 when avoiding using public transportation, practicing work/study from home, handshaking, and travelling to a red zone.Most respondents also strongly agree that if they are exposed to COVID-19, it will affect their health if they do not practice recommended behaviors (perceived severity -Table 3).Likewise, concerning perceived benefit (Table 3), more than 50% of respondents strongly agree that they can avoid getting COVID-19 if they practice these behaviors.In terms of perceived barriers, less than 20% felt that it would be challenging/difficult to implement the recommended behaviors (Table 3).Regarding self-efficacy, this is defined as the conviction that one can successfully practice a certain behavior, 13 the survey showed that less than 50% were confident that they could implement the recommended behavior (Table 3).
For each type of recommended behavior as mentioned in Table 1 to prevent the transmission of COVID-19, more than 50% of respondents never and rarely hosted, visited others, worked from the office/school, shook hands, travelled to a red zone, and left the house when not feeling well (Figure 2).
Based on the composite of each perceived item, it was found that only 16% of the respondents had a high percentage of good perceived susceptibility; perceived severity was 43%, perceived benefit was 54%, perceived barrier was 3%, selfefficacy was 19%, and only 60% of respondents practiced good behaviours.

Identify degree of freedom of structural model
The second step in SEM analysis is to run the identification of observed variables.A general requirement for identifying any type of model in SEM are the model's degrees of freedom which must be a least zero (df M ≥ 0).Hence, the solution to meet the requirement is to identify whether the model is under identified, just identified, or overidentified.Overidentified is mandatory in order to meet the requirement.An overidentified structural equation model is identified and has more observations than free parameters (df M >0) (2).In this study, we found the degree of freedom value to be 821, hence it was concluded that the model was over-identified.Thus, the next step of the analysis can be completed.Avoid travel to red zone (are with high positive rate) 5.0 7.2 13.0 23.9 50.9 Notes: 1: strongly disagree; 2: disagree; 3: neutral, 4: agree, 5: strongly agree.

Construct validity
Construct validity was performed to test whether the instrument or measurement variable could describe the latent variable correctly and precisely.For this, two tests were conducted: validity, which consisted of convergent and discriminant validity, and reliability. 15A convergent validity test examined the loading factor value of the measurement variable in each latent variable construct.If the loading factor value was greater than 0.50, the latent variable construct had good convergent validity. 15The results of the convergent validity test showed that almost all items in this study had a loading factor value of more than 0.5, except for items X11 and BHV3.These two items have a loading factor of ≤ 0.5, which indicates that they do not meet the criteria for convergent validity.Table 4 shows the AVE root value for each latent variable and the correlation coefficient between the latent variables.The value of the AVE root is shown as the value on the diagonal of the matrix, while the values beside and below the AVE root are the correlation coefficients between two pairs of variables.The results of the evaluation of discriminant validity show that the root value of AVE in each latent variable is greater than the correlation coefficient of the latent variable with other latent variables in the structural model.Thus, it can be stated that the items/instruments in this study have good discriminant validity.

Construct reliability
The construct reliability test was done by examining the composite reliability value.If the combined reliability value is greater than 0.7, it can be said that the variables in the study already have reliable indicators/question items. 19All latent variables were found to have a composite reliability value of more than 0.7, which means that each variable has a consistent measurement indicator and good internal consistency.

Structural model validity
Two analyses were conducted to evaluate the structural model validity:1) dependency test and 2) assessing the goodnessof-fit of the model.

Dependency test
The dependence relationship test was employed by looking at the path coefficient and its p-value in the structural model.The path coefficient shows the magnitude and direction of the relationship between the two variables.According to Table 5, sex had a statistically significant relationship with perceive susceptibility (p-value = 0.000); perceived susceptibility had a statistically significant relationship with perceived severity (p-value = 0.000); age (p-value = 0.004), sex (p-value = 0.030), and education ((p-value = 0.050) had significant relationships with perceived benefit; perceived benefit (p-value = 0.000), resident (p-value = 0.009), and sex (p-value = 0.000) had a significant relationship with perceive barrier; and perceive barrier (p-value = 0.000) and occupancy (p-value = 0.040) had a significant relationship with perceive self-efficacy.Regarding behavior, only perceived barriers (p-value = 0.000) and self-efficacy (p-value = 0.000) had statistically significant relationships with COVID-19 prevention behavior.The final structural model is as follows: behaviour = -0.018*susceptibility-0.041*severity -0.00045*benefit + 0.39*barrier -0.13*selfefficacy.The goodness-of-fit model assessment results in Table 6.showed that none met the goodness-of-fit criteria.Therefore, the structural model in this study was not fit for the data and was not fit to describe the empirical phenomenon under study.

Discussion
The results of categorized all perceived items revealed that only a small proportion of respondents (16%) held beliefs about their chances of experiencing COVID-19 (perceived susceptibility), while 43% of respondents believed in the severity of the COVID-19 effects were on their health.More than half of the respondents believed that the recommended behaviors to prevent COVID-19 infection could protect them from getting an infection (perceived benefit).Only 3% of respondents believed some things hindered the practice of recommended behaviors (perceive barrier), and only 19% believed that they could practice the recommended behaviors (perceive self-efficacy).This study's results are similar to those conducted in India, Sri Lanka, Iran, and Ethiopia. 8,10,17,20A study in Italiy conducted to nine hundred and seventyeight Italian adolescents found a similar pattern where they had a low perception of COVID-19 risk, as well as perceived comparative susceptibility and perceived seriousness.They think that COVID-19 is not a potentially severe disease for them as many news stated that young people are less vulnerable to the COVID-19 effect. 21The HBM theory holds that people are likely to practice preventive behaviors or actions if: 1) They believe that it will reduce their risks, 13 2) They perceive themselves as susceptible to COVID-19 infection, 3) They believe that this condition would have a potentially serious impact, 4) They acknowledge the benefit of recommended actions/behaviours in reducing the susceptibility or severity of the virus, and 5) They believe that the anticipated benefits of doing preventive behaviours/actions outweigh the barriers.
Perceived susceptibility was not a significant predictor of behavior in this study.This finding is consistent with studies that measured adherence to COVID-19 precautionary measures in China 22 and Korea. 23,24A study that used a HBM framework to look at how Saudi Arabian students at Jazan University felt about the COVID-19 vaccine found that perceived susceptibility was not a good predictor of how they felt about getting the vaccine. 25However, a similar study conducted in Malaysia found that high perceived susceptibility to COVID-19 infection was also associated with the behavior of vaccination intention. 26A study that measured student behavior in the US related to non-pharmaceutical interventions (hand washing with soap and water, use of hand sanitizer, wearing a face mask in public, and practicing social distancing) found that perceived susceptibility was associated with multiple interventions more frequently. 27previous study conducted by Du Min et al. found that low perceived risk was associated with vaccine hesitancy. 28s with perceived susceptibility, perceived severity was not a significant predictor of preventive behavior in this study.
Several studies regarding behavior change using BHM had similar results. 24,26,2724]29 Perceived benefit is one predictor that is not a significant predictor of preventive behavior.This finding is inconsistent with several studies that used BHM to predict behavior change, particularly in relation to COVID-19 prevention 8,22,23,[25][26][27][28][29] where it was found that there was no significant association between perceived benefit and preventive behaviour of.
This study demonstrated that the perceived barrier significantly predicted COVID-19 preventive behaviour.This result is contrasts with behaviour study conducted in Sri Lanka and Iran, which established a significant positive relationship between perceived benefit and self-efficacy in COVID-19 prevention behaviour. 10,30Nevertheless, these findings were congruent with a study conducted in Ethiopia that employed the HBM theory to assess student eating behaviour the US 31 and other behavioural studies in Iran, India, and Hong Kong. 8,17,32This study also found that perceived self-efficacy was a significant predictor of COVID-19 preventive behaviour.The results show that, with lower self-efficacy, people were likely to practice COVID-19 prevention behaviour.This result is similar to several studies that examined behaviours using the BHM theory.Those studies found that perceived self-efficacy has a significant relationship to behaviour. 10,17,20,23,30,33In contrast, a study that assessed the student's behaviour on the non-pharmaceutical intervention of COVID-19 found that perceived self-efficacy was not a significant predictor of behaviour change.In theory, an individual with good self-efficacy tends to practice recommended action/behaviour, 13 which is the preventive behaviour of COVID-19.However, this study was unable to confirm these findings.
The findings in this study illustrated that most respondents (97%) had no barriers to practising the recommended behaviour.Still most respondents (81%) were not confident that they could fully implement the recommended prevention behaviours.As many as 60% of respondents practised COVID-19 prevention behaviour well.In knowledge attitude practice (KAP) studies conducted in Indonesia, this finding (percentage of good behaviour) tended to be lower than the other two findings in Indonesia, where the rate of those who performed the correct behaviour for the prevention of COVID-19 was more than 90%. 34,35[38][39][40] Fundamentally, perceived susceptibility and severity affect how a person decides to act. 13However, most respondents (84%) had low perceived susceptibility in this study.This means that most respondents did not believe that they were also at risk of being affected by COVID-19.This perception represented an obstacle for someone to implement recommended behaviour.It was also known that only 43% believed that if they were infected with COVID-19, they would experience harmful consequences for their bodies.Only half of the respondents believed that the recommended behaviour was able to protect them from COVID-19.This might relate to the information they obtained day-to-day.It was possible that most of them did not have a clear idea about the pathophysiology and epidemiology of COVID-19, made worse by unreliable news or hoaxes circulated on social media, which may have increased negative perceptions about COVID-19. 8,41garding obstacles to implementing the recommended behaviour, a few respondents said it was extremely difficult to implement the behaviours (3%).This overall perception then leads to a low belief that respondents are able to implement the recommended behaviour.As a consequence, it will affect COVID-19 prevention behaviour practice.Perception is theoretically influenced by many factors, including demographics and level of knowledge. 8,10,13,20,34,35A study conducted in China found that knowledge was influenced by educational level and domicile. 36Good knowledge can form a good attitude, which then creates a good perceived. 42,43COVID-19 was a pandemic that touched almost every facet of human existence.People had to adjust their daily routines to accommodate local government rules in order to reduce the virus transmission, and these behavioural shifts may last long after the disease has passed. 44Increasing the respondents' knowledge is essential to narrowing the gap between knowledge and practice, including myths, hoaxes circulating about COVID-19, and facts.Several important factors that may affect perceived self-efficacy are related to social norms and trust 45 in the community.Unfortunately, neither of these factors (including knowledge) was investigated in this study.
The structural model of COVID-19 prevention behaviour in this study was not intended to describe the empirical phenomenon of preventive behaviour.Byrne stated that, if possible, researchers are advised to modify the model by using modification indices (MI) in SEM testing.He said that models with an MI score of more than 10 deserve attention for modification. 46Already it has proved that to confront the increase in demand for care, the need for long-term care workforce, and the costs associated with care. 47However, Hair et al. suggested that modifying the model should not change the model's structure significantly. 19ese several studies found a gap in knowledge to inform changes in policies on infection-prevention measures in the community, community infection procedures, the frequency of testing, etc. 8,41,48 The unreliable information may increase the negative perception of COVID-19, leading to the community's obedience to suggested preventive behaviors. 41Moreover, people are more worried about their families and economic conditions due to the spreading pandemic than about complying with lockdown or restriction policies.Several factors that might be related were not examined in this study, such as level of knowledge, social norms, or trust.

Study limitations
Due to the social restrictions (the outdoor community activities are limited by the government based on the law announced every two weeks except for emergency purposes) in Indonesia, the data collection was conducted using a Google form without using a selected sampling method.Thus, the total number of respondents in this study was not representative of the total population in Indonesia and the entire territory of Indonesia.In addition, the entire population of Indonesia did not have the same opportunity to be selected as respondents due to limitations related to internet coverage and utilities.Other than that, since this study used google form to collect the data, researcher really relied on the respondent's honesty and integrity.In order to limit the study respondents, the researchers already put inclusion criteria in the initial explanation section of the questionnaire that only those who are living in Indonesia and over 18 years old are eligible for the study.Therefore, we did not do any special treatment to control double entry and the location of respondents whether they live in Indonesia or outside the country.However, when we found it located outside of the country, we dropped the data from the data set.Because of the conditions described above, this study was vulnerable to information bias and selection bias.Regarding to the data collection process, we did not restrict respondent to just entry the form one time (in the system) so that there was possibility that one person will entry multiple times using different identity.Similar treatment conducted to clean the data, when researcher found double mobile number, we will randomly select one data and deleted the rest.We recommend to limit participants with "one email for one entry" to minimize the bias.As addition, this study can only describe the COVID-19 in just one time shot while the rate and impact of COVID-19 were changing rapidly over time.We recommend to employ longitudinal study for further since it can observe individual behavior change pattern/trend.

Conclusions and recommendation
This study found that more than half of the respondents still had low perceived susceptibility (84%) and severity (67%), but more than half had high perceived benefits (54%).Only a few respondents had significant barriers to implementing COVID-19 transmission prevention behaviours (3%).Still, most respondents had low perceived self-efficacy (81%), and only 60% had good behaviors related to COVID-19 prevention.
It was found that sex had a statistically significant relationship with perceive susceptibility (p-value = 0.000); perceived susceptibility had a statistically significant relationship with perceived severity (p-value = 0.000); age (p-value = 0.004), sex (p-value = 0.030), and education ((p-value = 0.050) had significant relationships with perceived benefit; perceived benefit (p-value = 0.000), resident (p-value = 0.009), and sex (p-value = 0.000) had a significant relationship with perceive barrier; and perceive barrier (p-value = 0.000) and occupancy (p-value = 0.040) had a significant relationship with perceive self-efficacy.Regarding behaviour, only perceived barriers (p-value = 0.000) and self-efficacy (p-value = 0.000) had statistically significant relationships with COVID-19 prevention behaviour.However, the structural model in this study was not fit to the data and was not fit to describe the empirical phenomenon under study.
This study can be an input for public health policy development especially those related to behaviour change interventions/programs.By using HBM theory, policy makers or other stakeholders can consider which stages of behaviour change still require more intervention in addition to demographic factors which can also influence it.Based on HBM theory, for behaviour change to occur, individuals must perceive a threat in their current behaviour (perceived susceptibility and severity), believe that the change will bring meaningful or useful outcomes (perceived benefit), and possess the self-confidence to enact the change (self-efficacy).In this case, this research can be a reminder in terms of making evidence-based policies.
In the context of COVID-19 preventive behaviour change, we recommend to improve perceived susceptibility and severity (since this study found low of perceived and severity) by providing the correct information about COVID-19 in the local cultural context.It is expected that by improving perceived susceptibility and severity, there would be an increase in respondents' knowledge, increasing perceived susceptibility and severity.The results and concept of this study can also be used/implemented for developing prevention policies against many types of diseases that require community behaviour changes which consider stages of behaviour change.
For further study, it is highly recommended to make inclusion and exclusion criteria prior to the data collection, to create a more rigorous data collection template to reduce selection bias effect, to collect the data onsite and if it is possible, to conduct the longitudinal study.The concept of HBM is one of the recommended theories to study about the health behaviour, hence it is replicable even in the different context of disease and area.

Ethics approval and consent to participate
The Commission approved this study for Research Ethics and Public Health Service, Faculty of Public Health, University of Indonesia Number: Ket-436/UN2.F10.D11/PPM.00.02/2021.

Consent for publication
Informed consent was obtained from all subjects involved in the study.
• DATA_FINAL_tio-edit2.csv (Data include all variables in the questionnaire.The data contain 1802 respondents and 89 variables.The variable names of questions 1-23 were given according to the keyword in each question, while the variable names for question number 24-35 were specified according to the question's number and its answer option.It is recommended to read the variable code definition in sheet 2 and the area code in sheet 3.) This project contains the following supplementary material: • Kuesioner Penelitian.pdf (the google questionnaire used in this study), https://doi.org/10.6084/m9.figshare.23292686.v2. 50ta are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).
Disease in Indonesia, 2021-2022: Structural Equation Modelling" provides a valuable contribution to the understanding of COVID-19 perceptions and preventive behaviors in Indonesia.The use of HBM and SEM provides a robust framework for their analysis.However, addressing the revisions outlined above will significantly strengthen the manuscript and improve its suitability for indexed.The Authors should provide a broader theoretical basis for their SEM analysis.The HBM is a well-established model, but the authors need to explicitly link their research questions and hypotheses to the specific constructs of the HBM.In addition, they should discuss how the HBM influenced their choice of variables and the relationships tested in their SEM model.

1.
The Authors need to provide more details about their data collection methods.In particular, they should address how they ensured that respondents did not complete the Google form multiple times and how they ensured an even distribution of respondents across the 38 provinces of Indonesia.If these measures were not taken, this point should be acknowledged as a limitation of the study.

2.
The goodness-of-fit indices suggest that the proposed model does not adequately fit the data.The authors should consider revising the model by exploring alternative constructs or pathways that may better represent the empirical data.

3.
The Authors are required to provide a detailed explanation of the steps taken to address issues with the model fit and any changes that were made to improve the model.

4.
The Authors should expand the discussion of the implications of their findings, with particular focus on the non significant predictors, and discuss how these findings contribute to our understanding of preventive behaviors in the context of COVID-19.

5.
I suggest including references to studies that have examined the perceived health risk associated with COVID-19 in different populations, such as adolescents and college students, to provide a broader context and comparison for your findings.Some of these studies include: 10.3389/fpsyg.2020.559951;10.3390/ejihpe13080108.

6.
The manuscript should be thoroughly proofread to correct grammatical errors and improve its readability.

7.
Partly Are all the source data underlying the results available to ensure full reproducibility?Partly

Are the conclusions drawn adequately supported by the results? Partly
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Development in the life span and the impact of critical events such as the COVID-19 pandemic I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.
Author Response 27 Sep 2024

Tris Eryando
Response to no. 1: We propose a comprehensive approach to understanding behavior change using the Health Belief Model (HBM), as opposed to focusing to understand behaviour change partially.The HBM offers a holistic perspective on behaviour change processes.However, when utilizing HBM as a theoretical framework, there were latent variables that did not directly observed.These latent variables are represented by various indicators/observe variable.Consequently, Structural Equation Modelling (SEM) is the most suitable analytical method for examining relationships between variables (latent variable and measurement/indicator) within the HBM framework, as it allows for the analysis of both latent variables and the overall model as a single, cohesive unit.We added the additional statement in the article. 1.
Response to no. 2: We did not restrict respondents to just entry the form one time.However, once the data collected and stored in to the database, researcher did data checking particularly to identify double/triple number.The researcher selected one data randomly and deleted the rest with the same number.There was possibility that one person will entry multiple times using different identity.We added the additional statement in the article.

2.
Response to no. 3: Unfortunately, we didn't develop or explore the alternative construct after finding that the model did not fit the data since the aim of this study was to examine changes in community behaviour to prevent local COVID-19 transmissions and changes in community perceptions about the severity level of COVID-19.This study also intended to prove whether the construct proposed can predict the behaviour or not and the result showed that it could not.We did not reconstruct the model considering Hair et al. suggestion that modifying the model should not change the model's structure significantly.

3.
Response to no. 4: In order to propose the good model, we have explained the one by 4.
one in the paper which include identifying degree of freedom, construct validity, discriminant validity, construct reliability, dependency and goodness of fit index.During the process, we excluded 2 variable that disturbed the data.However, when the model builds and tested with the goodness of fit index, we did not continue to restructure the model.We refer to Hair et al. suggestion that modifying the model should not change the model's structure significantly.We already stated it in the paper.We added reference as suggested.5.

Competing Interests:
No competing interests were disclosed.

Version 1
Reviewer Report 22 September 2023 https://doi.org/10.5256/f1000research.148373.r201070media channels, resulting in thousands of responses.However, it is essential to address potential biases in data collection.How did you ensure that respondents didn't fill out the Google Form multiple times?Was there strict control over the form submissions? Additionally, you mentioned that respondents were spread across Indonesia.How did you ensure even distribution among Indonesia's 38 provinces?Please provide details or references for this distribution method.If you were unable to meet up with any of these criteria, list them as your study limitations.

Study and Sample (Minor Revision):
This research aims to reach the Indonesian public, including contributions from social media influencers.To ensure the research's representativeness, please clarify how you ensured that the sample accurately represents all Indonesians.Your respondents are identified as Indonesian citizens, but how did you verify their citizenship?What measures were in place to prevent participation by non-Indonesian residents?Consider addressing these potential sources of bias.If you were unable to meet up with any of these criteria, list them as your study limitations.

Conceptual Model (Major Revision):
This research employs the Health Belief Model (HBM) as the primary theoretical framework.It is crucial to emphasize that Structural Equation Modeling (SEM) relies on a well-established theory to validate models using real data.The direction of relationships in the model must align with theoretical support.Additionally, theoretical underpinnings are essential in determining the appropriate sample size for SEM and ensuring sample adequacy when testing a theory-based model.Please provide a more comprehensive, precise and concise theoretical foundation for your SEM analysis.

Grammatical Errors (Minor Revision):
Correct the English writing errors in the article generally.Moreover, consider updating the COVID-19 incidence rate beyond the end of 2021, extending it into 2022, to enhance the study's accuracy and encompassing quality.

Results:
The results section requires elaboration and clarification to explain how you arrived at the conclusion regarding the relationship between self-efficacy and perceived influence on preventive behavior for readers who not statistic expert to be able to read and understand while retaining the journal format.
Discussion: Expand on the implications of the results and how they relate to the theoretical framework.Discuss the broader significance of your findings in the context of COVID-19 prevention in Indonesia and HBM concept for clinical practice in local context.Also, consider inclusion of more recent similar COVID-19 incidence data in other country to buttress your results in this section.

Conclusion with Limitations and Implications:
Summarize the key findings, discuss the study's limitations as suggested above, and provide implications for clinical practice or public health initiatives, and future research to ascertain replicability of your work.

Indexing of the Paper:
The indexing of the paper is contingent upon addressing the major revisions outlined in the methodology and conceptual model sections.Minor revisions should also be completed in the abstract, study and sample, grammatical errors, and the inclusion of more recent COVID-19 incidence data in the discussion section.Once these revisions are made, the paper will be reevaluated for indexing.
Thus, this study is vulnerable to information and selection bias.All this information will be added in the article as our study limitation including our suggestion to minimize the bias for the further study.

Study and sample
As we already wrote down in limitation part, sample in this study is not representing Indonesian population.We only distributed the link as much as we can and even engage with social media influencer to speed up the distribution.We wanted to collect as much as we can during the data collection time with the minimum sample size as our benchmark.As mentioned previously, we cannot control response from other country, as well as double respondent, however when we did data cleaning, we dropped out respondent who are not located in Indonesia and having the same identity.Indeed, this situation somehow my lead to the information and selection bias.Hence, we also provided our recommendation for further study.

Conceptual model
This study employed HBM theory as the conceptual framework and the data was collected according to each variable mentioned in the theory.The framework was described in the article both in Figure 1 and Table 1.The point of using SEM is to test a theory by specifying a model that represent predictions of that theory among plausible constructs measured with appropriate observed variables.In this study, we tested the HBM theory using the collected data.The output of this analysis is to identify whether the analysis deals with substantive theoretical issues regardless of whether or not a model is retained.Thus, in principle, the analysis in this study did not highlight/emphasize the statistical relationship among 2 variables, instead a model as a whole.We will add the explanation in the paper, In the conclusion section, significant results have been attached to preventive behavior and community perceptions of the severity of COVID-19 disease, but to support objective data, it is necessary to attach data in the form of accurate numbers in accordance with the findings in the field.In the results section, you mentioned that there is a relationship between self efficacy and perceived influence on preventive behavior.How did you come to the conclusion that these variables are related?

Methods (major revision)
This is an interesting study because it is a large-scale study of the country of Indonesia.I have high hopes that this research will be successfully indexed so that it can be used as a reference by other studies in information about Community preventive behavior and perception on the severity of COVID-19 disease in Indonesia.As with other research articles that have been published in various journals, of course your research article needs to be improved to make it worth reading.
Your research obtained data from a google form that was distributed through various social media.You managed to get thousands of responses.But I am curious how can you ensure that one respondent does not fill in the google form more than once?Is there strict control over the filling of google forms by respondents?I hope that your data does not become biased because of this.
Then you mentioned that the respondents were spread across Indonesia.How do you ensure that the respondents are indeed spread evenly across Indonesia, which consists of 38 provinces?Perhaps you have your own reference in determining the distribution throughout Indonesia as you mentioned, please explain.

Study and Sample (minor revision)
This research expands to the Indonesian public with contributions from social media influencers.How do you ensure this research is representative of all Indonesians?Respondents participating in your research are Indonesian citizens.How do you ensure that the people filling in the data are Indonesian citizens?What if a foreigner residing in Indonesia receives and fills out a questionnaire from your research, how do you know?

Conceptual Model (major revision)
This research uses HBM as the main reference.What should be a common concern in using SEM must be based on a certain theory.This is because the function of SEM is to confirm the model formed on real data obtained in the field.In addition, the determination of the direction of the relationship built in the model must be theoretically corroborated.This will also affect the determination of the amount of data or research samples that are suitable for use in SEM and feasible in terms of sample adequacy in testing a theory-based model.

Minor revision
Correct the English writing errors in the article.I have marked them on the linked annotated article.For example, in the questionnaire table in the methods section, and in the discussion section.The COVID-19 incidence rate has been mentioned from mid-2021 to the end of 2021.To add to the accuracy of this study, the incidence rate of COVID-19 until 2022 should also be attached.Reviewer Expertise: Nutrition Department I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

Comments on this article Version 1
Author Response 06 Dec 2023

Tris Eryando
Thank you for your valuable inputs to this paper.We already revised almost all part according to your input.Following are our response:

Abstract
The abstract session will be revised according to the article's revision.The number will be put accordingly.The conclusion of relationship between self-efficacy and perceived was made based on statistical test.It will be added in the abstract.

Methodology
Due to the social restriction during the pandemic, the data was collected using google form.We did not do any specific treatment to limit the frequency of people submission as well as the respondents location.We also could not prevent respondent from other country to fill the link.However, when we did data cleaning, we erased data which is not located in Indonesia and having the same identity.Because of data in this study was collected through google link, the respondent indeed did not distributed evenly in all area in Indonesia and not to mention to represent and that is for sure could not represent all Indonesian population.Thus, this study is vulnerable to information and selection bias.All this information will be added in the article as our study limitation including our suggestion to minimize the bias for the further study.

Study and sample
As we already wrote down in limitation part, sample in this study is not representing Indonesian population.We only distributed the link as much as we can and even engage with social media influencer to speed up the distribution.We wanted to collect as much as we can during the data collection time with the minimum sample size as our benchmark.As mentioned previously, we cannot control response from other country, as well as double respondent, however when we did data cleaning, we dropped out respondent who are not located in Indonesia and having the same identity.Indeed, this situation somehow my lead to the information and selection bias.Hence, we also provided our recommendation for further study.

Conceptual model
This study employed HBM theory as the conceptual framework and the data was collected according to each variable mentioned in the theory.The framework was described in the article both in Figure 1 and Table 1.The point of using SEM is to test a theory by specifying a model that represent predictions of that theory among plausible constructs measured with appropriate observed variables.In this study, we tested the HBM theory using the collected data.The output of this analysis is to identify whether the analysis deals with substantive theoretical issues regardless of whether or not a model is retained.Thus, in principle, the analysis in this study did not highlight/emphasize the statistical relationship among 2 variables, instead a model as a whole.We will add the explanation in the paper, Grammatical Error, Result, Discussion, and Conclusion with Limitations and Implications >> we directly revised in the document The benefits of publishing with F1000Research: Your article is published within days, with no editorial bias • You can publish traditional articles, null/negative results, case reports, data notes and more • The peer review process is transparent and collaborative • Your article is indexed in PubMed after passing peer review • Dedicated customer support at every stage • For pre-submission enquiries, contact research@f1000.com : a. Draw model b.Input data in the form of covariance or correlation matrix c.Identify the model d.Assess parameter estimates e. Assess fit measure (chi-square, degree of freedom, residual matrix, GFI, RMSEA) f.Check the modification indices g.Rerun the model till we get the best fit of the data to the model ad theory.

Figure 1 .
Figure 1.Proposed structural model of community preventive behavior against COVID-19.15
Grammatical Error, Result, Discussion, and Conclusion with Limitations and Implications >> we directly revised in the document Competing Interests: No competing interests were disclosed.Reviewer Report 13 September 2023 https://doi.org/10.5256/f1000research.148373.r195846 3 Department of Nutrition, School of Medicine, Syiah Kuala University, Banda Aceh, Aceh, Indonesia 1. Abstract (minor revision) Is the work clearly and accurately presented and does it cite the current literature?YesIs the study design appropriate and is the work technically sound?YesAre sufficient details of methods and analysis provided to allow replication by others?YesIf applicable, is the statistical analysis and its interpretation appropriate?PartlyAre all the source data underlying the results available to ensure full reproducibility?YesAre the conclusions drawn adequately supported by the results?PartlyCompeting Interests: No competing interests were disclosed.

Table 1 .
The observed variables.
I am at risk when not keeping a distance of at least 1-2 metres when in the work area.X14 I have a low risk of getting COVID-19 if I practice avoiding travel to a red zone (an area with a high positive rate).
I may get COVID-19 and cause health impacts to myself if I do not follow behaviorsKeep distance at least 1-2 metre when at public

Table 4 .
Results of the discriminant validity test.

Table 5 .
Results of the SEM statistical test.

Table 6 .
Continued are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).Figshare.IMPLEMENTATION OF SOCIAL POLICY DISTANCING IN EFFORT PREVENTION COVID-19 IN INDONESIA.https://protect-us.mimecast.com/s/qsa7CkRwomfYPpGvjC2JN1U?domain=doi.org 15